The DOI matches its Crossref record; 100 of the 100 references it cites were found in a registry or an index.
Urban flow prediction from spatiotemporal data using machine learning: A survey
Abstract
Abstract Urban spatiotemporal flow prediction is of great importance to traffic management, land use, public safety. This prediction task is affected by several complex and dynamic factors, such as patterns of human activities, weather, events, and holidays. Datasets evaluated the flow come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data. To summarize these methodologies of urban flow prediction, in this paper, we first introduced four main factors affecting urban flow. Second, in order to further analyze urban flow, we partitioned the preparation process of multi-source spatiotemporal data related with urban flow into three groups. Third, we chose the spatiotemporal dynamic data as a case study for the urban flow prediction task. Fourth, we analyzed and compared some representative flow prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based, and transfer learning-based methods. Finally, we showed open challenges of urban flow prediction and discussed many recent research works on urban flow prediction. This paper will facilitate researchers to find suitable methods and public datasets for addressing urban spatiotemporal flow forecast problems.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,281
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Forecasting principles and practice
Hyndman, R. J., & Athanasopoulos, G. (2018) · Cited by 4,405
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Williams, R. J., & Zipser, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.2.270 · Cited by 4,369
Automatic Time Series Forecasting: The forecast Package for R
Hyndman, R. J., & Khandakar, Y. (2008) · Journal of Statistical Software · doi:10.18637/jss.v027.i03 · Cited by 3,511
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Yuan, H., & Li, G. (2021) · Data Science and Engineering · doi:10.1007/s41019-020-00151-z · Cited by 389
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A literature review of Artificial Intelligence applications in railway systems
Tang, R., De Donato, L., Bes̆inović, N., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., Tang, T., Vittorini, V., & Wang, Z. (2022) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2022.103679 · Cited by 324
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Metadata
- Authors
- Xie, P., Li, T., Liu, J., Du, S., Yang, X., & Zhang, J.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 59
- Pages
- 1-12
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W3000301417
- Cited by
- 300
- References
- 100
- FWCI
- 20.70
- Type
- journal article
- Language
- en
- Open access
- closed
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- Xie, P., Li, T., Liu, J., Du, S., Yang, X., & Zhang, J. (2020). Urban flow prediction from spatiotemporal data using machine learning: A survey. Information Fusion. https://doi.org/10.1016/j.inffus.2020.01.002
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